Rapid detection method for integration hot spot of exogenous genetic material insertion site

Abstract

The application discloses a rapid detection method of integration hot spots based on exogenous genetic material insertion sites, uniquely encodes chromosome-site information of all ISs to obtain a digital Y representing the chromosome and the integration position; sorts Ys corresponding to all ISs; for each IS in the insertion site data set, firstly, records the IS as a CIS node C; records other nodes Vi in the insertion site data set; if the distance between C and Vi is less than a threshold TH, records Vi added to the current CIS to become a node of the CIS; simultaneously, connects the edge of C and Vi; then, continues to return to step 2, continues the next IS, and sequentially obtains the CIS network; based on the connection between all ISs, the CIS network is searched to search for a CIS meeting the condition; the application optimizes the connection searching mode and the CIS searching range, and in the detection of several integration sites, the average analysis time of the software is greatly reduced, and the clinical practicability of the CIS detection algorithm is increased.

Description

Technical Field

[0001] This invention relates to the technical field of next-generation sequencing data analysis in the field of bioinformatics, specifically a rapid detection method for integration hotspots based on exogenous genetic material insertion sites. Background Technology

[0002] Gene therapy is a treatment method that uses molecular biology techniques to introduce exogenous DNA into the genome of gene-deficient cells to restore normal cellular function. Integrating vectors are a type of vector DNA sequence commonly used in gene therapy, used to load exogenous DNA fragments and integrate them into the host genome via insertion. When an integrating vector integrates into a specific site in the host genome, it may affect the normal function of genes near that site. In particular, when cancer-related genes are present near the integration site, the integration event may trigger the activation of proto-oncogenes, leading to cancer.

[0003] Currently, various side effects and carcinogenic phenomena caused by vector integration into the genome have been observed in preclinical studies and some clinical trials using viral vectors. Therefore, safety evaluation of vector insertion mutations is a crucial step in gene therapy, both before and during clinical trials, as well as after the product's market launch.

[0004] Normally, integrative vectors insert into the host genome randomly. Since cancer-related genes occupy a very small portion of the host genome, the probability of a single integration leading to cell carcinogenesis is extremely low. However, different types of integrative vectors exhibit regional biases, meaning that integration events occur frequently in certain specific regions of the host genome, increasing the mutation risk in those regions. Such regions are defined as common integration sites (CIS). If cancer-related genes are present near a CIS, it increases the risk of secondary damage to the patient on top of their existing disease.

[0005] NGS technology can detect hundreds of thousands of viral integration sites (IS) in a single experiment, thereby efficiently determining the regional bias of integration vectors and avoiding the carcinogenic risks introduced by systematic insertion bias in the early stages of drug administration. In 2011, Ulrich first provided a rigorous mathematical definition of CIS and compared the distribution patterns of CIS of two different integration vectors from a statistical perspective. This method focuses on the statistical analysis of the regional bias of integration vectors at the macro level, without describing the individual CIS. In 2016, Raffaele and colleagues developed a method for extracting CIS based on network graphs. This method sorts IS, reduces the time required for CIS detection, and presents the relationships between sites within CIS in a visual manner.

[0006] The specific method for detecting CIS using this algorithm is as follows: Step 1: Perform operations on each chromosome; Step 2: Sort the Y values ​​corresponding to all ISs; Step 3: For each IS in the ordered insertion site dataset, Step 3.1: Record the IS as a CIS node C; Step 3.2: For other nodes Vi in the ordered insertion site dataset; Step 3.2.1: If the distance between C and Vi is less than the threshold TH; Record Vi and add it to the current CIS, making it a node of that CIS; Connect the edges of C and Vi; If the distance between C and Vi is greater than the threshold TH; Continue from Step 3.2 (next IS); Continue from Step 2 (next IS); Finally, continue to the next chromosome. Due to the limitations of the underlying network structure, this method is only suitable for finding all CISs in a small number of integration sites and cannot efficiently filter CISs based on size. In practical applications, it is necessary to distinguish between trend clustering and random overlap of ISs based on the size of the CIS.

[0007] Furthermore, current methods do not analyze the potential carcinogenic risk and network topology characteristics of CIS from a functional perspective, failing to fully explore the clinical value of CIS analysis. The computational burden of current methods increases exponentially with the number of integration sites. In a single CIS detection of 200,000 IS, current methods take over 24 hours, limiting their practical application. Current methods do not compare the positional relationships of CIS with key genes and their functional regions, thus failing to quantify the impact of specific CIS events on the genome or their carcinogenic risk, limiting the clinical usefulness of CIS. Analyzing the internal structure of CIS and summarizing its characteristics is crucial for a deeper understanding of CIS and helps predict its carcinogenic mechanisms; however, no methods currently address the analysis and summarization of the internal structural characteristics of CIS. Summary of the Invention

[0008] To address the shortcomings of existing technologies, current methods do not analyze the potential carcinogenic risk and network topology characteristics of integration sites (IS) from a functional perspective, thus failing to fully explore the clinical value of CIS analysis. The computational load of current methods increases exponentially with the number of integration sites. In a single CIS detection of 200,000 IS, current methods take over 24 hours, limiting their practical application. Current methods do not compare the positional relationships between CIS and key genes and their functional regions, thus failing to quantify the impact of specific CIS events on the genome or their carcinogenic risk, limiting the clinical usefulness of CIS. Analyzing the internal structure of CIS and summarizing its characteristics is crucial for further understanding CIS and helps predict its carcinogenic mechanisms. However, current methods lack the capacity to analyze and summarize the internal structural characteristics of CIS. This invention provides a rapid detection method for integration hotspots based on exogenous genetic material insertion sites.

[0009] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0010] This invention relates to a rapid detection method for integration hotspots based on exogenous genetic material insertion sites, comprising the following steps:

[0011] Step 1: Uniquely encode the chromosome-site information of all IS to obtain a number Y that simultaneously represents the chromosome and the integration location;

[0012] Step 2: Sort the Y values ​​corresponding to all IS values;

[0013] Step 3: For each IS in the dataset with insertion sites; first, record the IS as a CIS node C; record other nodes Vi in the dataset with insertion sites; if the distance between C and Vi is less than the threshold TH, record Vi and add it to the current CIS, making it a node of the CIS; at the same time, connect C and Vi at the edge; then return to step 2 and continue to the next IS, and so on to obtain the CIS network;

[0014] Step 4: Based on the connections between all IS-IS, form a CIS network and search for CISs that meet the criteria.

[0015] As a preferred technical solution of the present invention, the method for uniquely encoding the chromosome-site information of all IS is to replace the 24 chromosome numbers with a number vector A of length 24, and ensure that the difference between any two numbers in A is much greater than the largest number of sites on any chromosome; then, for each integration site, the converted chromosome number A is added to the number of sites at the integration position on the chromosome to obtain a number Y that simultaneously represents the chromosome and the integration position.

[0016] As a preferred embodiment of the present invention, the method for finding a CIS that meets the conditions based on the connection relationships between all IS-IS is as follows:

[0017] Step 4.1: Based on the formed CIS network structure, find IS sites with a connection count greater than a certain threshold n, and use them as seed sites S;

[0018] Step 4.2: For a seed site S, find all sites S1 that are directly connected to the seed site S and record them; record all IS within the connection to obtain (S+S1);

[0019] Step 4.3: For each site in S1, find all sites S2 that are directly connected to S1 and record them. If all S2 sites already exist in (S+S1), then the search is complete.

[0020] Step 4.4: If at least one S2 does not exist in (S+S1); find all sites S2 that are directly connected to S1 and record them...; repeat this process until the number of new sites no longer increases, thus completing the CIS search.

[0021] Step 5: After identifying all CIS, CISAT analyzes the clustering locations of CIS on the genome, as well as the genes and their functional regions near the CIS locations, to determine the extent of CIS's impact on the genome and its carcinogenic risk.

[0022] As a preferred embodiment of the present invention, the method by which CISAT analyzes the clustering location of CIS on the genome, as well as the genes and their functional regions near the CIS location, involves: first, the distribution of CIS across the entire genome; and second, cancer-related genes, genes related to serious adverse events, and their functional regions. Through literature review, known oncogenes and genes related to serious adverse events are statistically analyzed to form a knowledge base. Then, it is determined whether there are CIS within a certain range near known genes with specific functions. If there is a CIS near an oncogene, CISAT will analyze the function of that gene.

[0023] As a preferred technical solution of the present invention, after the detection of CIS is completed, the internal structure of the CIS network is analyzed and features are extracted based on the topological properties of the CIS network. The features extracted by CISAT include the number of IS sites, the degree of the central site, the total number of connections, the average degree of the network, the average path length, the maximum diameter, the occurrence rate and the clustering coefficient. These features reflect the occurrence rate of each CIS on the host genome, the scope of its influence on the host genome, the mode of influence and the degree of influence from multiple perspectives.

[0024] The beneficial effects of this invention are:

[0025] This rapid detection method for integration hotspots based on exogenous genetic material insertion sites optimizes computational resource consumption in two ways. First, CISAT filters CISs based on the higher frequency of connections within them, searching only for potential CISs that meet the criteria, rather than searching the entire network structure. Second, by setting seed sites, CISAT ensures that the CIS network structure can be constructed with fewer iterations during the search process. Furthermore, this invention optimizes the connection lookup method and the CIS search range. In the detection of several integration sites, the average analysis time of the software is significantly reduced, increasing the clinical applicability of this CIS detection algorithm. Attached Figure Description

[0026] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0027] In the attached diagram:

[0028] Figure 1 This is a flowchart illustrating the rapid detection method for integration hotspots based on exogenous genetic material insertion sites according to the present invention.

[0029] Figure 2 This is a schematic diagram of the visualization results of the top 10 CIS with the highest total number of connections by CISAT in an embodiment of the rapid detection method for integration hotspots based on the insertion site of exogenous genetic material in this invention.

[0030] Figure 3 This is an example of the rapid detection method for integration hotspots based on exogenous genetic material insertion sites in this invention, showing the location distribution of CIS on the genome;

[0031] Figure 4 This is a comparison diagram of the actual distribution and simulated random IS distribution of the integration hotspot rapid detection method based on the insertion site of exogenous genetic material in this invention;

[0032] Figure 5 This is a comparison chart of the required running time before and after optimization for input files with different numbers of IS in an embodiment of the rapid detection method for integration hotspots based on exogenous genetic material insertion sites of the present invention.

[0033] Figure 6 This is a schematic diagram of the top 10 CIS and their topological features in Example 2 of the rapid detection method for integration hotspots based on exogenous genetic material insertion sites of the present invention. Detailed Implementation

[0034] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0035] Example: Figures 1 to 6 As shown, this invention presents a rapid detection method for integration hotspots based on exogenous genetic material insertion sites. The CISAT algorithm consists of two stages: CIS detection and CIS description. In the CIS detection stage, CISAT primarily performs statistical analysis on integration sites within the input file. After merging, sorting, and networking operations, it outputs all CIS sites in the sample that meet predetermined criteria. In the description stage, CISAT assesses the host's risk of cancer and serious adverse events based on the CIS output from the previous stage, considering the relative positions of each CIS with cancer-related genes, genes associated with serious adverse events, and their functional regions. Furthermore, it quantitatively describes the internal structural characteristics of CIS based on metrics such as node distance and topology within the CIS network.

[0036] CISAT takes as input a set of viral ISs, each IS indicating its integration location on the host genome. During CIS detection, CISAT first merges nearby ISs to prevent site errors caused by sequencing bias. Then, based on the distance between integration sites, ISs with a distance less than a certain threshold T are connected, forming the basis for drawing the IS network structure. To avoid searching for all possible connections through traversal and string matching, CISAT improves the original algorithm as follows: First, it encodes the chromosome-site information of all ISs, simplifying it to a single number, rather than a combination of chromosome strings and numbers representing chromosome sites. Specifically, the 24 chromosome numbers are replaced by a 24-bit number vector A, ensuring that the difference between any two numbers in A is much greater than the maximum number of sites on any chromosome. Then, for each integration site, the converted chromosome number A is added to the number of sites on the chromosome where the integration occurred, resulting in a number Y representing both the chromosome and the integration site (e.g., converting chr5-123456 to 8700000123456). Finally, sort the Y corresponding to all IS. After sorting, for any IS, sites with a distance less than the threshold T from that site must only exist near that site. Furthermore, if the distance between Yi and Yi+1 is greater than T, then the distance between Yi and all sites after Yi+1 must also be greater than T. After sorting, CISAT can find all connections with a single traversal, reducing the time complexity from O(n^2) to O(nlogn).

[0037] The specific steps are as follows:

[0038] Step 1: Uniquely encode the chromosome-site information of all IS to obtain a number Y that simultaneously represents the chromosome and the integration location;

[0039] Step 2: Sort the Y values ​​corresponding to all IS values;

[0040] Step 3: For each IS in the dataset with insertion sites; first, record the IS as a CIS node C; record other nodes Vi in the dataset with insertion sites; if the distance between C and Vi is less than the threshold TH, record Vi and add it to the current CIS, making it a node of the CIS; at the same time, connect C and Vi at the edge; then return to step 2 and continue to the next IS, and so on to obtain the CIS network;

[0041] Step 4: Based on the connections between all IS-IS, form a CIS network and search for CISs that meet the criteria.

[0042] After finding all connections, the next step is to find CISs that meet the criteria based on the connections between all IS-IS. According to the definition of a CIS, multiple IS sites will cluster within a certain region; therefore, the connection frequency between IS sites within a CIS is higher than in non-CIS regions. Based on this characteristic, CISAT cleverly utilizes a seed IS selection method, extracting only CISs that meet the criteria, avoiding the search of the entire network structure and reducing unnecessary computation compared to previous methods. In practice, CISAT first statistically analyzes the connections between ISs to find IS sites with a connection frequency greater than n (n can be user-defined based on actual needs), and uses these as the set of seed IS sites S for potential CIS regions. Then, for each site S, CISAT searches for all sites S1 directly connected to S (S1 may be multiple), and then searches for all sites S2 directly connected to S1, iterating in this way. After each iteration, the number of newly added sites in this CIS is determined until the number of newly added sites no longer increases, thus completing the construction of the CIS network based on the seed site S. Compared to existing algorithms, CISAT optimizes computational resource consumption in two ways. First, CISAT filters CIS based on features with higher internal connection frequencies, searching only for potential CIS that meet the criteria, rather than searching the entire network structure. Second, by setting seed sites, CISAT ensures that the CIS network structure can be constructed with fewer iterations during the search process. After the CIS is constructed, CISAT uses existing visualization tools to visualize its internal structure.

[0043] The method for finding a CIS that meets the criteria based on the connection relationships between all IS-IS is as follows:

[0044] Step 4.1: Based on the formed CIS network structure, find IS sites with a connection count greater than a certain threshold n, and use them as seed sites S;

[0045] Step 4.2: For a seed site S, find all sites S1 that are directly connected to the seed site S and record them; record all IS within the connection to obtain (S+S1);

[0046] Step 4.3: For each site in S1, find all sites S2 that are directly connected to S1 and record them. If all S2 sites already exist in (S+S1), then the search is complete.

[0047] Step 4.4: If at least one S2 does not exist in (S+S1); find all sites S2 directly connected to S1 and record them...; and so on; until the number of new sites no longer increases; complete the CIS search.

[0048] Step 5: After identifying all CIS, CISAT analyzes the clustering locations of CIS on the genome, as well as the genes and their functional regions near the CIS locations, to determine the extent of CIS's impact on the genome and its carcinogenic risk.

[0049] The CISAT method for analyzing the clustering locations of CIS on the genome, as well as the genes and their functional regions near the CIS locations, involves two steps: first, the distribution of CIS across the entire genome; and second, cancer-related genes, genes associated with serious adverse events, and their functional regions. Through literature review, known oncogenes and genes associated with serious adverse events are statistically analyzed to form a knowledge base. Then, it determines whether CIS exists within a certain range near known genes with specific functions. If CIS is found near an oncogene, CISAT will determine the distribution location of the CIS within the gene's functional region to infer whether the CIS will affect the normal function of the gene (e.g., Figure 3 and Figure 4 (As shown).

[0050] in Figure 3 The diagram shows the location distribution of CIS on the genome in this example, with denser distribution areas shown in darker colors. The gene examples highlight the distribution of the top 10 CIS with the highest total number of connections on the chromosome.

[0051] Figure 4This indicates that CISAT determines the distribution of CIS within the functional region of this gene and compares it with computer-simulated randomized sites. The vertical dashed line represents the transcription start site (TSS), the x-axis represents the region surrounding the FANCA gene's TSS and the internal region of the gene, and the y-axis represents the proportion of sequence support for the integration site within that region relative to the total sequence support for the sample. The gray bars represent the computer-simulated randomized IS distribution, and the dark bars represent the actual distribution of CIS.

[0052] After the detection of CIS is completed, the internal structure of the CIS network is analyzed and features are extracted based on the topological properties of the CIS network. The features extracted by CISAT include the number of IS sites, the degree of the central site, the total number of connections, the average degree of the network, the average path length, the maximum diameter, the occurrence rate, and the clustering coefficient. These features reflect the occurrence rate of each CIS on the host genome, the scope of its influence on the host genome, the mode of influence, and the degree of influence from multiple perspectives.

[0053] Specifically, for practical operation, a specific embodiment 1 is provided.

[0054] Its main function is to compare the running time required for detection under different data volumes before and after optimization. The 12 samples were divided into 4 groups, each containing 3 samples. 1000, 10000, 100000, and 200000 IS were randomly selected from each group, respectively. Then, the same data was detected using both the unoptimized and optimized CIS detection algorithms. Finally, the detection time for each sample was recorded and compared. The results are shown in Table 1 below.

[0055] Table 1 compares the running time required for detection before and after optimization under different data volumes.

[0056]

[0057] When the input file contained 1000 IS records, the average time before optimization was 0.79 minutes, and after optimization, it was 0.38 minutes, meaning the average time before optimization was 2.08 times that after optimization. When the number of IS records in the input file increased to 100,000, the average detection time before optimization increased to 594.5 minutes, while the detection time after optimization was 11.8 minutes for the same data, meaning the average time before optimization was 50.4 times that after optimization. Figure 5 It can be seen that before optimization, the detection time and the number of IS (Instant Interpretations) showed an basically exponential growth relationship. However, after optimization, the increase in detection time with the number of IS slowed down significantly. CISAT essentially solved the limitation of the original method in practical applications where the detection time was too long.

[0058] The purpose of Example 2 is to further confirm the detection range of CISAT for CIS and to illustrate the importance of the newly added detection dimension of CIS topological features for a deeper understanding of CIS. Therefore, we used experimental data from a real lentiviral integration site experiment to test the CISAT analysis results to obtain complete analysis results. We further refined the description of CIS based on the differences in CIS topological features and illustrated and compared the impact of different CIS features on the genome with examples.

[0059] CISAT first detects CISs from the IS list. After detection, CISAT visualizes the detected CISs in two dimensions. The first dimension is to draw a network diagram of a single CIS (e.g., Figure 2 The second dimension is to mark the specific location of the CIS on the corresponding position on the chromosome (e.g., Figure 3 ).from Figure 3 As can be seen, the top 10 CISs with the highest total connections detected by CISAT can all be found in the chromosome map, and the IS density at the corresponding locations is significantly deeper than that of the surrounding areas, indicating that CISAT can display the CIS locations at the chromosome level.

[0060] CISAT also analyzed CIS at the level of oncogenes and SAE genes to determine the carcinogenic risk and risk of serious adverse events for each CIS. Based on the location of each CIS, CISAT identified the potential genes affected, compared them with genes in a knowledge base, and analyzed the functional region distribution on the major genes of the CIS. In this embodiment, one of the top 10 CIS-affected genes with the highest total number of connections was the oncogene FANCA (…). Figure 6 CISAT determined the impact of CIS on gene functional regions, with FANCA as the main gene. This helps determine how CIS affects the function of the FANCA gene. Figure 4 It can be seen that the CIS is mainly concentrated upstream of the TSS of the FANCA gene, indicating that the CIS near FANCA is more likely to affect the gene's transcriptional function by influencing the promoter region upstream of the TSS, while the coding region of the FANCA gene has only a small probability of being affected by the CIS (e.g., Figure 4 (As shown).

[0061] Finally, CISAT analyzed the topological characteristics of the top 10 CISs in terms of total connections in Example 2, which helps determine the scope and degree of influence of CISs on the host genome. A comparison between CISs represented by the POLR2A gene and those represented by the F8 gene illustrates the importance of topological characteristics for further understanding CISs. POLR2A has fewer IS sites than F8, but more total connections, resulting in a clustering coefficient 1.69 times that of F8. This indicates that POLR2A has a higher density of internal IS sites and a higher frequency of damage to the genome within the CIS. However, in terms of the scope of influence, POLR2A's influence is 62 kbp, while F8's is 147 kbp, more than twice that of POLR2A. Comparing the network morphology of the CISs corresponding to F8 and POLR2A also reveals that F8's network is a chain-like connection, with ISs at both ends of the CIS connected through intermediate links, while POLR2A's network is a central connection, with most ISs directly connected, indicating that the distances between ISs are closer. Overall, F8 has a wider impact range, but POLR2A is more likely to cause damage to the genome within its range.

[0062] From a dichotomy perspective, the dichotomy of the CIS represented by the PRR12 gene is 0.86, and its aggregation coefficient is only 0.73. This indicates that although the internal connections of this CIS are not tight, it tends to form two sub-network structures, and the connections between ISs in the sub-network structures are tighter. This may suggest that this CIS is composed of two local CISs. Based on the positional relationship of the genes affected by PRR12, this CIS may be composed of three CISs: SCAF1, PRR12, and NOSIP.

[0063] In summary, this method overcomes the limitations of CIS detection time and, by combining the topological properties of CIS, quantifies the impact of specific CIS events on the genome or the risk of cancer, thereby increasing the clinical significance of CIS analysis.

[0064] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for rapid detection of integration hot spots based on insertion sites of exogenous genetic material, characterized by: Includes the following steps, Step 1: Uniquely encode the chromosome-site information of all IS to obtain a number Y that simultaneously represents the chromosome and the integration location; Step 2: Sort the Y values ​​corresponding to all IS values; Step 3: For each IS in the dataset with insertion sites; first, record the IS as a CIS node C; record the other nodes Vi in the dataset with insertion sites; If the distance between C and Vi is less than the threshold TH, record Vi and add it to the current CIS to become a node of the CIS; at the same time, connect C and Vi at the edge; then continue to step 2 and continue to the next IS, and so on to obtain the CIS network; Step 4: Based on the connections between all IS-IS, form a CIS network and search for CISs that meet the criteria; The method for uniquely encoding the chromosome-site information of all IS is to replace the 24 chromosome numbers with a number vector A of length 24, and ensure that the difference between any two numbers in A is much greater than the maximum number of sites on any chromosome; then, for each integration site, the converted chromosome number A is added to the number of sites at the integration position on the chromosome to obtain a number Y that simultaneously represents the chromosome and the integration position. The method described above for finding CISs that meet the criteria based on the connection relationships between all IS-ISs is as follows: Step 4.1: Based on the formed CIS network structure, find IS sites with a connection count greater than a certain threshold n, and use them as seed sites S; Step 4.2: For a given seed site S, find all sites S1 that are directly connected to the seed site S and record them; record all IS within the connection to obtain (S+S1). Step 4.3: For each site in S1, find all sites S2 that are directly connected to S1 and record them. If all S2 sites already exist in (S+S1), then the search is complete. Step 4.4: If at least one S2 does not exist in (S+S1); find all sites S2 directly connected to S1 and record them...; repeat this process until the number of newly added sites no longer increases; complete the CIS search; Step 4.5: After identifying all CIS, CISAT analyzes the clustering locations of CIS on the genome, as well as the genes and their functional regions near the CIS locations, to determine the extent of CIS's impact on the genome and its carcinogenic risk.

2. The method for rapid detection of integration hot spot based on insertion site of exogenous genetic material according to claim 1, characterized in that, The method described in CISAT for analyzing the clustering location of CIS on the genome, as well as the genes and their functional regions near the CIS location, involves two steps: first, the distribution of CIS across the entire genome; second, cancer-related genes, genes related to serious adverse events, and their functional regions. Through literature review, known oncogenes and genes related to serious adverse events are statistically analyzed to form a knowledge base. Then, it is determined whether there are CIS within a certain range near known genes with specific functions. If there are CIS near an oncogene, CISAT will determine the distribution location of the CIS within the functional region of that gene to infer whether the CIS will affect the normal function of that gene.

3. The method for rapid detection of integration hot spot based on insertion site of exogenous genetic material according to claim 1, characterized in that, After the detection of CIS is completed, the internal structure of the CIS network is analyzed and features are extracted based on the topological properties of the CIS network. The features extracted by CISAT include the number of IS sites, the degree of the central site, the total number of connections, the average degree of the network, the average path length, the maximum diameter, the occurrence rate, and the clustering coefficient. These features reflect the occurrence rate of each CIS on the host genome, the scope of its influence on the host genome, the mode of influence, and the degree of influence from multiple perspectives.

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